Development and evaluation of a general approach for predicting pathogen decay in surface waters using hierarchical Bayesian modeling

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-08-10 DOI:10.1016/j.envsoft.2024.106183
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Abstract

A general approach for predicting indicator and pathogen decay in surface waters was developed using Bayesian hierarchical modeling, a persistence database, and a two-parameter model form. The resulting hierarchical regression describes general persistence behaviors with target-level intercepts and population-level coefficients. Uncertainty factors calculated with the approach suggest fecal indicator bacteria (FIB) and pathogenic bacteria persist similarly in surface waters, but median virus and protozoa persistence metrics may be 2–3 times greater than FIB in similar conditions. The two-parameter model underpinning the approach was used to identify drivers of these differences. Virus decay rates were shown to taper off more quickly than FIB, whereas protozoa were associated with longer initial periods of minimal decay. Despite the low accuracy of the hierarchical model compared to models fit to individual datasets, this approach addresses a critical gap for water management decision-making as site-specific and pathogen-specific persistence data are uncommon in water monitoring practices.

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利用分层贝叶斯建模预测地表水中病原体衰变的通用方法的开发与评估
利用贝叶斯分层建模、持久性数据库和双参数模型形式,开发了一种预测地表水中指标和病原体衰变的通用方法。由此产生的分层回归描述了具有目标水平截距和种群水平系数的一般持久性行为。用这种方法计算出的不确定性系数表明,粪便指示细菌(FIB)和致病细菌在地表水中的持久性类似,但病毒和原生动物的持久性指标中值在类似条件下可能比粪便指示细菌大 2-3 倍。该方法所依据的双参数模型被用来确定这些差异的驱动因素。结果表明,病毒的衰减速度比 FIB 更快,而原生动物则与较长的初始最小衰减期有关。尽管与适合单个数据集的模型相比,分层模型的准确性较低,但这种方法弥补了水管理决策中的一个重要缺口,因为在水监测实践中,特定地点和特定病原体的持久性数据并不常见。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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